Figure 5 R Notebook

suppressPackageStartupMessages({
  library(Seurat)
  library(org.Dr.eg.db)
  library(BSgenome.Drerio.UCSC.danRer11)
  library(Signac)
  library(knitr)
  library(kableExtra)
  library(dplyr)
  library(ggplot2)
  library(ggsci)
  library(limma)
  library(JASPAR2020)
  library(patchwork)
  library(TFBSTools)
  library(motifmatchr)
  library(AnnotationHub)
  library(harmony)
})
options(future.globals.maxSize = 4000 * 1024^2)

mypal <- pal_igv(palette = "default",alpha = 1)(30)

2. Read data

HB13hpf <- readRDS(file = "RDSfiles/HB13hpf_neural.RDS")
DefaultAssay(HB13hpf) <- "SCT"
Idents(HB13hpf) <- "Clusters"
HB16hpf <- readRDS(file = "RDSfiles/HB16hpf_neural.RDS")
DefaultAssay(HB16hpf) <- "SCT"
Idents(HB16hpf) <- "Clusters"

HB.int <- readRDS(file = "RDSfiles/int.neural.3WT.subset.RDS")
DefaultAssay(HB.int) <- "SCT"
DimPlot(HB.int, reduction = "wnn.umap") + scale_color_igv()

HB.int$intClusters <- as.character(HB.int$intClusters)
HB.int$intClusters[HB.int$intClusters %in% c("r1&r2.1","r1&r2.2")] <- "r1&r2"
HB.int$intClusters[HB.int$intClusters %in% c("r3.1")] <- "r3"
HB.int$intClusters[HB.int$intClusters %in% c("r4.1","r4.2")] <- "r4"
HB.int$intClusters[HB.int$intClusters %in% c("r5.1","r5.2")] <- "r5"
HB.int$intClusters[HB.int$intClusters %in% c("r6.1","r6.2")] <- "r6"
HB.int$intClusters <- droplevels(as.factor(HB.int$intClusters))
Idents(HB.int) <- "intClusters"
levels(HB.int)

 [1] "CaudHB.1"      "CaudHB.2"      "CaudHB.3"      "CaudHB.4"      "Ciliated"      "HB"            "MB.1"         
 [8] "MB.2"          "MB.3"          "MHB.1"         "MHB.2"         "MHB.3"         "MHB.4"         "MHB.5"        
[15] "MHB.6"         "Mitochondrial" "NC.1"          "NC.2"          "Neurog"        "Neuron.1"      "Neuron.2"     
[22] "r1"            "r1&r2"         "r2"            "r3"            "r4"            "r5"            "r6"           
[29] "SC.1"          "SC.2"          "SC.3"          "SC.4"         

levels(HB.int) <- c("r1","r1&r2","r2","r3","r4","r5","r6","CaudHB.1","CaudHB.2","CaudHB.3","CaudHB.4","Ciliated","HB","MB.1","MB.2",
                    "MB.3","MHB.1","MHB.2","MHB.3","MHB.4","MHB.5","MHB.6","Mitochondrial","NC.1","NC.2","Neurog","Neuron.1", 
                    "Neuron.2","SC.1","SC.2","SC.3","SC.4")

3. Heatmaps

3.1 HB13hpf top5 DE genes

All.markers.13 <- FindAllMarkers(HB13hpf, only.pos = T, verbose = F)

top5.pval.13 <- All.markers.13 %>% group_by(cluster) %>% top_n(n=-5, wt = p_val) %>% top_n(n=5, wt = avg_log2FC)
top5.pval.13

Idents(HB13hpf) <- "Clusters"
HB13hpf <- RenameIdents(HB13hpf, "FB.1" = "FB.1       ")
heatmapPlot.13 <- DoHeatmap(HB13hpf, features = unique(top5.pval.13$gene), group.colors = mypal, 
                         size = 5, angle = 45) + 
  guides(color = FALSE) +
  theme(axis.text = element_blank())
heatmapPlot.13

3.2 HB16hpf top5 DE genes

All.markers.16 <- FindAllMarkers(HB16hpf, only.pos = T, verbose = F)

top5.pval.16 <- All.markers.16 %>% group_by(cluster) %>% top_n(n=-5, wt = p_val) %>% top_n(n=5, wt = avg_log2FC)
top5.pval.16

heatmapPlot.16 <- DoHeatmap(HB16hpf, features = unique(top5.pval.16$gene), group.colors = mypal, 
                         size = 5, angle = 45) + 
  guides(color = FALSE) +
  theme(axis.text = element_blank())
heatmapPlot.16

3. Compare r1, r3 & r5 to r2, r4 and r6

r135vs246_markers <- FindMarkers(HB.int, 
                                 ident.1 = c("r1","r3","r5"), 
                                 ident.2 = c("r2","r4","r6"), verbose = FALSE)

avg_r135vs246 <- AverageExpression(HB.int, features = rownames(r135vs246_markers), assays = "SCT")$SCT
avg_r135vs246 <- as.data.frame(avg_r135vs246) %>%
  select(r1, r2, r3, r4, r5, r6)
r135vs246_m_avg <- cbind(r135vs246_markers,avg_r135vs246)

r135over246_m_avg <- r135vs246_m_avg %>%
  filter(p_val_adj < 0.05 & avg_log2FC > 0.5 & r1 > 0 & r3 > 0 & r5 > 0)
r135over246_m_avg

r246over135_m_avg <- r135vs246_m_avg %>%
  filter(p_val_adj < 0.05 & avg_log2FC < -0.5 & r2 > 0 & r4 > 0 & r6 > 0)
r246over135_m_avg

3.1 r1/r3/r5 over r2/r4/r6

p135 <- VlnPlot(HB.int, features = rownames(r135over246_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)), 
        idents = c("r1","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend() +
  theme(axis.title.x = element_blank())
p135

kableExtra::kbl(as.data.frame(round(AverageExpression(HB.int, assays = "SCT", slot = "data", 
                        features = rownames(r135over246_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
  dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
  kable_classic_2(full_width = F)

	r1	r1&r2	r2	r3	r4	r5	r6
egr2b	0.014	0.117	0.000	3.611	0.053	4.873	0.000
epha4a	0.348	0.640	0.687	10.365	0.773	9.833	0.222
sema3fb	0.203	0.883	0.179	6.476	0.523	6.683	0.089
timp2b	0.087	0.252	0.119	2.802	0.030	2.937	0.067
nab1b	0.101	0.180	0.060	1.317	0.114	1.063	0.100
myo1cb	0.058	0.009	0.000	1.048	0.030	1.262	0.011
sema3ab	0.014	0.027	0.015	0.254	0.008	1.468	0.033
midn	0.522	1.126	1.269	3.429	1.030	4.508	1.444
gria1a	0.014	0.027	0.045	0.206	0.008	1.405	0.067
brinp2	0.043	0.054	0.090	0.849	0.053	0.810	0.033
tenm3	1.246	1.144	0.657	1.270	0.742	2.603	0.978
ackr3b	0.319	0.351	0.313	0.810	0.371	2.571	0.856
tox3	0.710	0.946	1.403	2.397	0.598	3.325	2.000
epha4l	0.319	0.829	0.552	1.365	0.561	2.095	0.856
ctnnd2b	1.014	0.973	0.970	1.810	1.212	2.238	0.589
sdk2b	0.072	0.198	0.119	0.675	0.076	0.683	0.078
celf2	1.957	1.144	2.209	1.270	1.811	4.603	0.767

3.1 r2/r4/r6 over r1/r3/r5

p246 <- VlnPlot(HB.int, features = rownames(r246over135_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)), 
        idents = c("r1","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend() +
  theme(axis.title.x = element_blank())
p246

kableExtra::kbl(as.data.frame(round(AverageExpression(HB.int, assays = "SCT", slot = "data", 
                        features = rownames(r246over135_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
  dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
  kable_classic_2(full_width = F)

	r1	r1&r2	r2	r3	r4	r5	r6
col7a1l	0.029	1.216	0.806	0.246	2.242	0.246	1.611
efnb3b	3.478	3.982	4.149	0.889	3.765	1.675	5.478
epha4b	2.304	2.883	4.284	1.857	5.379	2.889	7.322
plxna2	0.130	0.432	1.537	0.286	0.992	0.175	0.322
ddr1	1.551	1.180	3.000	1.190	2.318	1.659	2.944
qkib	0.783	0.802	1.030	1.000	2.583	1.127	1.878

4. Compare r1 & r2 to rest

r12vsRest_markers <- FindMarkers(HB.int, 
                                 ident.1 = c("r1","r1&r2","r2"), 
                                 ident.2 = c("r3","r5","r4","r6"), verbose = FALSE)

avg_r12vsRest <- AverageExpression(HB.int, features = rownames(r12vsRest_markers), assays = "SCT")$SCT
avg_r12vsRest <- as.data.frame(avg_r12vsRest) %>%
  select(r1, r2, r3, r4, r5, r6)
r12vsRest_m_avg <- cbind(r12vsRest_markers,avg_r12vsRest)

r12overRest_m_avg <- r12vsRest_m_avg %>%
  filter(p_val_adj < 0.05 & avg_log2FC > 0.5 & r1 > 0 & r2 > 0)
r12overRest_m_avg

# Restoverr12_m_avg <- r12vsRest_m_avg %>%
#   filter(p_val_adj < 0.05 & avg_log2FC < -0.5 & r3 > 0 & r4 > 0 & r5 > 0 & r6 > 0)
# Restoverr12_m_avg

4.1 r1/r2 over r3/r4/r5/r6

p12 <- VlnPlot(HB.int, features = rownames(r12overRest_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)), 
        idents = c("r1","r1&r2","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend() +
  theme(axis.title.x = element_blank())
p12

kableExtra::kbl(as.data.frame(round(AverageExpression(HB.int, assays = "SCT", slot = "data", 
                        features = rownames(r12overRest_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
  dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
  kable_classic_2(full_width = F)

	r1	r1&r2	r2	r3	r4	r5	r6
efna2a	8.826	4.288	2.224	1.556	1.886	0.817	0.733
bmpr1ba	3.000	2.369	2.657	1.198	1.576	0.778	1.089
ek1	0.507	0.829	0.716	0.190	0.212	0.111	0.156
nrp2a	1.072	0.982	0.149	0.024	0.167	0.048	0.078
ptprn2	3.246	1.468	1.821	0.698	1.091	0.548	0.822
nr2f1b	1.609	1.288	0.806	0.206	0.705	0.063	0.256
rasal2	5.174	4.901	4.194	2.548	3.205	2.786	3.089
fgfr2	6.232	5.793	3.851	2.294	3.667	2.881	4.467
si:ch211-286o17.1	2.217	1.568	1.701	0.563	0.720	0.571	1.033
pdzrn3b	0.957	0.730	0.373	0.103	0.212	0.127	0.133
bahcc1b	1.333	2.414	0.985	0.984	1.447	0.421	0.556
arhgap29a	0.696	1.153	0.955	0.484	0.455	0.262	0.178
ror1	1.290	2.432	1.552	1.595	1.303	0.571	0.367

4.2 r3/r4/r5/r6 over r1/r2

# VlnPlot(HB.int, features = rownames(Restoverr12_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)), 
#         idents = c("r1","r1&r2","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend()

# kableExtra::kbl(as.data.frame(round(AverageExpression(HB.int, assays = "SCT", slot = "data", 
#                         features = rownames(Restoverr12_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
#   dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
#   kable_classic_2(full_width = F)

5. Compare r3 & r4 to rest

r34vsRest_markers <- FindMarkers(HB.int, 
                                 ident.1 = c("r3","r4"), 
                                 ident.2 = c("r1","r1&r2","r2","r5","r6"), verbose = FALSE)

avg_r34vsRest <- AverageExpression(HB.int, features = rownames(r34vsRest_markers), assays = "SCT")$SCT
avg_r34vsRest <- as.data.frame(avg_r34vsRest) %>%
  select(r1, r2, r3, r4, r5, r6)
r34vsRest_m_avg <- cbind(r34vsRest_markers,avg_r34vsRest)

r34overRest_m_avg <- r34vsRest_m_avg %>%
  filter(p_val_adj < 0.05 & avg_log2FC > 0.5 & r3 > 0 & r4 > 0)
r34overRest_m_avg

# Restoverr34_m_avg <- r34vsRest_m_avg %>%
#   filter(p_val_adj < 0.05 & avg_log2FC < -0.5 & r1 > 0 & r2 > 0 & r5 > 0 & r6 > 0)
# Restoverr34_m_avg

5.1 r3/r4 over r1/r2/r5/r6

p34 <- VlnPlot(HB.int, features = rownames(r34overRest_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)), 
        idents = c("r1","r1&r2","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend() +
  theme(axis.title.x = element_blank())
p34

kableExtra::kbl(as.data.frame(round(AverageExpression(HB.int, assays = "SCT", slot = "data", 
                        features = rownames(r34overRest_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
  dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
  kable_classic_2(full_width = F)

	r1	r1&r2	r2	r3	r4	r5	r6
cyp26b1	0.174	0.802	1.642	2.190	3.508	0.151	0.022
cntfr	7.493	6.135	12.552	11.579	13.288	8.968	6.933
epha4a	0.348	0.640	0.687	10.365	0.773	9.833	0.222
cadm4	0.145	0.288	0.343	0.659	1.144	0.167	0.400
sema3fb	0.203	0.883	0.179	6.476	0.523	6.683	0.089

5.2 r1/r2/r5/r6 over r3/r4

# VlnPlot(HB.int, features = rownames(Restoverr34_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)), 
#         idents = c("r1","r1&r2","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend()

# kableExtra::kbl(as.data.frame(round(AverageExpression(HB.int, assays = "SCT", slot = "data", 
#                         features = rownames(Restoverr34_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
#   dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
#   kable_classic_2(full_width = F)

6. Compare r5 & r6 to rest

r56vsRest_markers <- FindMarkers(HB.int, 
                                 ident.1 = c("r5","r6"), 
                                 ident.2 = c("r1","r1&r2","r2","r3","r4"), verbose = FALSE)

avg_r56vsRest <- AverageExpression(HB.int, features = rownames(r56vsRest_markers), assays = "SCT")$SCT
avg_r56vsRest <- as.data.frame(avg_r56vsRest) %>%
  select(r1, r2, r3, r4, r5, r6)
r56vsRest_m_avg <- cbind(r56vsRest_markers,avg_r56vsRest)

r56overRest_m_avg <- r56vsRest_m_avg %>%
  filter(p_val_adj < 0.05 & avg_log2FC > 0.5 & r5 > 0 & r6 > 0)
r56overRest_m_avg

# Restoverr56_m_avg <- r56vsRest_m_avg %>%
#   filter(p_val_adj < 0.05 & avg_log2FC < -0.5 & r1 > 0 & r2 > 0 & r3 > 0 & r4 > 0)
# Restoverr56_m_avg

6.1 r5/r6 over r1/r2/r3/r4

p56 <- VlnPlot(HB.int, features = rownames(r56overRest_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)), 
        idents = c("r1","r1&r2","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend() +
  theme(axis.title.x = element_blank())
p56

kableExtra::kbl(as.data.frame(
  round(AverageExpression(HB.int, assays = "SCT", slot = "data", 
                        features = rownames(r56overRest_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
  dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
  kable_classic_2(full_width = F)

	r1	r1&r2	r2	r3	r4	r5	r6
hoxa4a	0.000	0.072	0.015	0.000	0.015	3.778	2.367
mafba	0.029	0.144	0.000	0.016	0.114	2.865	3.356
hoxb3a	0.043	0.135	0.000	0.508	0.652	3.381	2.789
evx1	0.014	0.036	0.000	0.016	0.061	0.825	0.456
si:ch211-216b21.2	0.058	0.243	0.045	0.111	0.015	1.238	0.878
tenm4	3.899	5.486	9.507	3.746	1.955	8.389	12.033
gria1a	0.014	0.027	0.045	0.206	0.008	1.405	0.067
col11a1b	0.072	0.081	0.119	0.095	0.114	0.802	1.489
hoxd3a	0.029	0.090	0.000	0.008	0.015	0.087	1.122
ptprua	0.101	0.135	0.179	0.151	0.106	2.444	0.411
sema3ab	0.014	0.027	0.015	0.254	0.008	1.468	0.033
cyp26c1	0.696	1.018	1.388	0.302	0.818	1.802	5.467
ackr3b	0.319	0.351	0.313	0.810	0.371	2.571	0.856
dclk2a	0.275	0.270	0.179	0.548	0.258	1.175	1.467
antxr1c	0.101	0.135	0.075	0.071	0.114	0.508	1.000
tshz3b	0.029	0.045	0.045	0.063	0.492	0.929	0.544
zgc:158328	0.029	0.117	0.194	0.087	0.500	0.738	1.633
tiam1a	0.594	1.036	1.119	0.690	0.848	2.413	1.400
cxcl12a	0.319	0.333	1.507	0.333	0.970	1.563	1.900
nhsl1a	0.043	0.216	0.134	0.111	0.061	0.460	0.956
iqca1	0.101	0.081	0.030	0.262	0.045	0.881	0.289
inavaa	0.014	0.477	0.164	0.635	0.485	2.143	1.089
tox3	0.710	0.946	1.403	2.397	0.598	3.325	2.000
cbfa2t2	0.464	0.135	0.194	0.452	0.227	1.135	0.700
rarab	0.696	0.811	0.567	0.825	1.114	1.675	1.878
nck2a	1.000	1.892	1.955	2.238	1.250	3.976	2.944
nos1apa.1	1.855	1.333	1.493	1.746	1.485	2.659	3.700
dennd5b	0.841	1.000	1.209	0.524	0.735	1.849	2.233
magi2a	1.029	0.757	1.343	0.849	0.909	3.262	2.000
map7d1a	2.014	1.568	1.299	0.746	1.447	2.214	3.067
adgrl1a	0.420	1.333	0.507	0.857	2.394	2.881	3.378
rnf165a	0.710	1.216	0.627	0.984	1.106	2.302	1.911
vat1	0.174	0.324	0.642	0.341	0.409	1.063	0.911
col15a1b	0.145	0.667	0.254	0.254	0.492	1.802	0.822
chl1a	0.261	0.297	0.090	0.111	0.076	0.310	1.378
midn	0.522	1.126	1.269	3.429	1.030	4.508	1.444
dbn1	0.522	0.865	0.582	1.254	1.258	2.159	2.422
dtnba	0.696	0.559	1.940	1.333	1.803	2.349	2.400
timp2b	0.087	0.252	0.119	2.802	0.030	2.937	0.067
adgrl3.1	0.696	0.730	0.642	0.849	0.894	1.786	1.211
epha4l	0.319	0.829	0.552	1.365	0.561	2.095	0.856
ctnna2	0.275	0.324	0.552	0.349	0.917	1.119	1.644
col12a1a	0.391	0.477	0.448	0.381	0.348	0.810	1.911
sema3fb	0.203	0.883	0.179	6.476	0.523	6.683	0.089
epha4a	0.348	0.640	0.687	10.365	0.773	9.833	0.222
tenm3	1.246	1.144	0.657	1.270	0.742	2.603	0.978
jag1a	0.130	0.036	0.045	0.151	0.045	0.857	0.333

6.2 r1/r2/r3/r4 over r5/r6

# VlnPlot(HB.int, features = rownames(Restoverr56_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)), 
#         idents = c("r1","r1&r2","r2","r3","r4","r5","r6"), stack = TRUE, flip = TRUE, fill.by = "ident", cols = mypal) + NoLegend() 

# kableExtra::kbl(as.data.frame(
#   round(AverageExpression(HB.int, assays = "SCT", slot = "data", 
#                         features = rownames(Restoverr56_m_avg %>% filter(p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)))$SCT,3)) %>%
#   dplyr::select(r1, 'r1&r2', r2, r3, r4, r5, r6)) %>%
#   kable_classic_2(full_width = F)

write.table(r135vs246_m_avg, file = "../results/rhom_DEgenes_135vs246.txt", sep = "\t", quote = FALSE, col.names = NA)
write.table(r12vsRest_m_avg, file = "../results/rhom_DEgenes_12vs3456.txt", sep = "\t", quote = FALSE, col.names = NA)
write.table(r34vsRest_m_avg, file = "../results/rhom_DEgenes_34vs1256.txt", sep = "\t", quote = FALSE, col.names = NA)
write.table(r56vsRest_m_avg, file = "../results/rhom_DEgenes_56vs1234.txt", sep = "\t", quote = FALSE, col.names = NA)

combined <- 
  (((heatmapPlot.13) +
      #plot_spacer() +
     (heatmapPlot.16) #+ 
     #plot_layout(widths = c(2,0.1,2))
  ) /
  ((plot_spacer()) +
     ((p135 / plot_spacer() / p246 / plot_spacer() / p12 / plot_spacer() / p34) + 
        plot_layout(heights = c(17,0.1,6,0.1,13,0.1,5))) + 
     #plot_spacer() +
     (p56) +
     plot_layout(widths = c(3,1,1))
  )) + 
  plot_layout(heights = c(1,1.5))
combined

ggsave(filename = "../results/Fig5_combinedPlot.png", plot = combined)

Saving 31.2 x 31.2 in image

sessionInfo()

R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS 12.3

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats4    stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] harmony_0.1.0                       Rcpp_1.0.7                          AnnotationHub_3.2.0                
 [4] BiocFileCache_2.2.0                 dbplyr_2.1.1                        motifmatchr_1.16.0                 
 [7] TFBSTools_1.32.0                    patchwork_1.1.2                     JASPAR2020_0.99.10                 
[10] limma_3.50.3                        ggsci_2.9                           ggplot2_3.4.0                      
[13] dplyr_1.0.7                         kableExtra_1.3.4                    knitr_1.36                         
[16] Signac_1.2.1                        BSgenome.Drerio.UCSC.danRer11_1.4.2 BSgenome_1.62.0                    
[19] rtracklayer_1.54.0                  Biostrings_2.62.0                   XVector_0.34.0                     
[22] GenomicRanges_1.46.0                GenomeInfoDb_1.30.0                 org.Dr.eg.db_3.14.0                
[25] AnnotationDbi_1.56.1                IRanges_2.28.0                      S4Vectors_0.32.4                   
[28] Biobase_2.54.0                      BiocGenerics_0.40.0                 SeuratObject_4.0.4                 
[31] Seurat_4.0.1                       

loaded via a namespace (and not attached):
  [1] rappdirs_0.3.3                SnowballC_0.7.0               scattermore_0.7              
  [4] R.methodsS3_1.8.1             tidyr_1.1.4                   bit64_4.0.5                  
  [7] irlba_2.3.3                   DelayedArray_0.20.0           R.utils_2.11.0               
 [10] data.table_1.14.2             rpart_4.1-15                  KEGGREST_1.34.0              
 [13] RCurl_1.98-1.5                generics_0.1.1                cowplot_1.1.1                
 [16] RSQLite_2.2.8                 RANN_2.6.1                    future_1.26.1                
 [19] bit_4.0.4                     tzdb_0.2.0                    spatstat.data_2.1-0          
 [22] webshot_0.5.4                 xml2_1.3.3                    httpuv_1.6.3                 
 [25] SummarizedExperiment_1.24.0   assertthat_0.2.1              DirichletMultinomial_1.36.0  
 [28] xfun_0.27                     hms_1.1.1                     jquerylib_0.1.4              
 [31] evaluate_0.14                 promises_1.2.0.1              fansi_0.5.0                  
 [34] restfulr_0.0.13               caTools_1.18.2                igraph_1.2.8                 
 [37] DBI_1.1.1                     htmlwidgets_1.5.4             sparsesvd_0.2                
 [40] spatstat.geom_2.3-0           purrr_0.3.4                   ellipsis_0.3.2               
 [43] annotate_1.72.0               deldir_1.0-6                  MatrixGenerics_1.6.0         
 [46] vctrs_0.5.0                   ROCR_1.0-11                   abind_1.4-5                  
 [49] cachem_1.0.6                  withr_2.5.0                   ggforce_0.3.3                
 [52] sctransform_0.3.3             GenomicAlignments_1.30.0      goftest_1.2-3                
 [55] svglite_2.1.0                 cluster_2.1.2                 ape_5.6-2                    
 [58] lazyeval_0.2.2                seqLogo_1.60.0                crayon_1.4.2                 
 [61] labeling_0.4.2                pkgconfig_2.0.3               slam_0.1-48                  
 [64] tweenr_1.0.2                  nlme_3.1-153                  rlang_1.0.6                  
 [67] globals_0.15.1                lifecycle_1.0.3               miniUI_0.1.1.1               
 [70] filelock_1.0.2                polyclip_1.10-0               matrixStats_0.61.0           
 [73] lmtest_0.9-38                 Matrix_1.3-4                  ggseqlogo_0.1                
 [76] zoo_1.8-9                     ggridges_0.5.3                png_0.1-7                    
 [79] viridisLite_0.4.0             rjson_0.2.20                  bitops_1.0-7                 
 [82] R.oo_1.24.0                   KernSmooth_2.23-20            blob_1.2.2                   
 [85] stringr_1.4.0                 parallelly_1.32.0             readr_2.0.2                  
 [88] CNEr_1.30.0                   scales_1.2.1                  memoise_2.0.0                
 [91] magrittr_2.0.1                plyr_1.8.6                    ica_1.0-2                    
 [94] zlibbioc_1.40.0               compiler_4.1.0                BiocIO_1.4.0                 
 [97] RColorBrewer_1.1-2            fitdistrplus_1.1-6            Rsamtools_2.10.0             
[100] cli_3.4.1                     listenv_0.8.0                 pbapply_1.5-0                
[103] MASS_7.3-54                   mgcv_1.8-38                   tidyselect_1.1.1             
[106] stringi_1.7.5                 highr_0.9                     yaml_2.2.1                   
[109] ggrepel_0.9.1                 grid_4.1.0                    sass_0.4.0                   
[112] fastmatch_1.1-3               tools_4.1.0                   future.apply_1.8.1           
[115] parallel_4.1.0                rstudioapi_0.13               TFMPvalue_0.0.8              
[118] lsa_0.73.2                    gridExtra_2.3                 farver_2.1.0                 
[121] Rtsne_0.15                    digest_0.6.28                 BiocManager_1.30.19          
[124] shiny_1.7.1                   pracma_2.3.3                  qlcMatrix_0.9.7              
[127] BiocVersion_3.14.0            later_1.3.0                   RcppAnnoy_0.0.19             
[130] httr_1.4.2                    colorspace_2.0-2              rvest_1.0.3                  
[133] XML_3.99-0.8                  tensor_1.5                    reticulate_1.22              
[136] splines_4.1.0                 uwot_0.1.10                   RcppRoll_0.3.0               
[139] spatstat.utils_2.2-0          renv_0.15.5                   plotly_4.10.0                
[142] systemfonts_1.0.4             xtable_1.8-4                  jsonlite_1.7.2               
[145] poweRlaw_0.70.6               R6_2.5.1                      pillar_1.6.4                 
[148] htmltools_0.5.2               mime_0.12                     glue_1.6.2                   
[151] fastmap_1.1.0                 BiocParallel_1.28.0           interactiveDisplayBase_1.32.0
[154] codetools_0.2-18              utf8_1.2.2                    lattice_0.20-45              
[157] bslib_0.3.1                   spatstat.sparse_2.0-0         tibble_3.1.6                 
[160] curl_4.3.2                    leiden_0.3.9                  gtools_3.9.2                 
[163] GO.db_3.14.0                  survival_3.2-13               rmarkdown_2.11               
[166] docopt_0.7.1                  munsell_0.5.0                 GenomeInfoDbData_1.2.7       
[169] reshape2_1.4.4                gtable_0.3.0                  spatstat.core_2.3-0